Verbal autopsy methods with multiple causes of death
Verbal autopsy methods with multiple causes of death
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DOI：
10.1214/07-sts247
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发表时间：
2008-02-01
影响因子：
5.7
通讯作者：
Lu, Ying
中科院分区：
文献类型：
--
作者：
King, Gary;Lu, Ying
在没有医疗死亡证明的地区，口头尸检程序被广泛用于估计特定原因的死亡率。从医疗机构收集护理人员报告的症状数据以及死因，并在仅可获得症状数据的人群中估计死因分布。目前的方法一次仅分析一个原因，涉及被认为难以或不可能满足的假设，并且需要昂贵、耗时或不可靠的医生审查、专家算法或参数统计模型。通过概括当前分析多种原因的方法，我们展示了如何放弃现有方法背后的大多数困难假设。这些概括也使得医生审查、专家算法和参数统计假设变得不必要。通过理论结果以及对中国和坦桑尼亚数据的实证分析，我们说明了这种方法的准确性。虽然没有一种分析口头尸检数据的方法，包括这里提供的计算密集型方法，可以在所有情况下给出准确的估计，但所提供的程序在概念上更简单、更便宜、更通用、可复制或更可复制，并且比现有方法更容易在实践中使用。我们还展示了我们对估计总体比例的关注，即口头尸检研究中主要感兴趣的数量，也可能大大减少该领域和其他领域的许多个体分类器所需的假设，从而提高其性能。作为本文的配套文件，我们还提供了易于使用的软件来实现本文讨论的方法。
Verbal autopsy procedures are widely used for estimating cause-specific mortality in areas without medical death certification. Data on symptoms reported by caregivers along with the cause of death are collected from a medical facility, and the cause-of-death distribution is estimated in the population where only symptom data are available. Current approaches analyze only one cause at a time, involve assumptions judged difficult or impossible to satisfy, and require expensive, time-consuming, or unreliable physician reviews, expert algorithms, or parametric statistical models. By generalizing current approaches to analyze multiple causes, we show how most of the difficult assumptions underlying existing methods can be dropped. These generalizations also make physician review, expert algorithms and parametric statistical assumptions unnecessary. With theoretical results, and empirical analyses in data from China and Tanzania, we illustrate the accuracy of this approach. While no method of analyzing verbal autopsy data, including the more computationally intensive approach offered here, can give accurate estimates in all circumstances, the procedure offered is conceptually simpler, less expensive, more general, as or more replicable, and easier to use in practice than existing approaches. We also show how our focus on estimating aggregate proportions, which are the quantities of primary interest in verbal autopsy studies, may also greatly reduce the assumptions necessary for, and thus improve the performance of, many individual classifiers in this and other areas. As a companion to this paper, we also offer easy-to-use software that implements the methods discussed herein.